System and method for image localization based on semantic segmentation

ABSTRACT

A system and method for image localization based on semantic segmentation are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on an autonomous vehicle; performing semantic segmentation or other object detection on the received image data to identify and label objects in the image data and produce semantic label image data; identifying extraneous objects in the semantic label image data; removing the extraneous objects from the semantic label image data; comparing the semantic label image data to a baseline semantic label map; and determining a vehicle location of the autonomous vehicle based on information in a matching baseline semantic label map.

PRIORITY PATENT APPLICATION

This patent application is a continuation patent application drawingpriority from U.S. non-provisional patent application Ser. No.15/598,727; filed May 18, 2017. This present non-provisional patentapplication draws priority from the referenced patent application. Theentire disclosure of the referenced patent application is consideredpart of the disclosure of the present application and is herebyincorporated by reference herein in its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the U.S. Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the disclosure hereinand to the drawings that form a part of this document: Copyright2016-2020, TuSimple, All Rights Reserved.

TECHNICAL FIELD

This patent document pertains generally to tools (systems, apparatuses,methodologies, computer program products, etc.) for image processing,vehicle localization, vehicle navigation, vehicle control systems, andautonomous driving systems, and more particularly, but not by way oflimitation, to a system and method for image localization based onsemantic segmentation.

BACKGROUND

Image semantic segmentation is intended to identify the image regionscorresponding directly to objects in an image by labeling each pixel inthe image to a semantic category. Contrary to the object recognitionwhich merely detects the objects in the image, semantic segmentationassigns a category label to each pixel to indicate an object to whichthe pixel belongs. As such, semantic segmentation aims to assign acategorical label to every pixel in an image, which plays an importantrole in image analysis and self-driving systems.

Localization is a process of determining an exact location of a vehiclein an environment. Localization is important for navigating the vehiclewithin the environment. Localization is also important for avoidingobstacles in the environment. In some cases, localization can be moredifficult when using semantic segmentation labeling because of varyingextraneous objects appearing in the images upon which the semanticsegmentation labeling was performed. Additionally, the accuracy of theimage data and distance data from the sensor devices on an autonomousvehicle may be less than optimal. As such, there can be problems ingenerating an accurate vehicle position and velocity.

SUMMARY

Various example embodiments disclosed herein describe a system andmethod for highly automated localization and navigation for autonomousvehicles using semantic parsing. The system and method of an exampleembodiment comprise two main components or phases: 1) a mappingcomponent/phase, and 2) a localization component/phase. In the mappingphase, image data from one or multiple cameras (or other imagegenerating devices) is sent to a computing device within the systemwhile a test vehicle is operating. The computing device processes theimage data to produce a highly accurate baseline semantic label imageand remove extraneous dynamic objects from the baseline semantic labelimage, which is recorded as a baseline semantic label map or route maprepresentation in a data storage device. In the localization phase, asecond computing device in an autonomous vehicle computes the samesemantic label image based on live image data and removes extraneousdynamic objects from the semantic label image. The second computingdevice then localizes the vehicle's position by comparing the similarityof the semantic label image to the baseline semantic label map. Themethod disclosed herein includes: 1) removing extraneous dynamic objectsfrom a semantic label image; and 2) localizing a vehicle's position bycomparing the similarity of a semantic label image to a baselinesemantic label map.

BRIEF DESCRIPTION OF THE DRAWINGS

The various embodiments are illustrated by way of example, and not byway of limitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates a block diagram of an example ecosystem in which animage processing and localization module of an example embodiment can beimplemented;

FIGS. 2 and 3 illustrates the components of the image processing andlocalization module of an example embodiment;

FIG. 4 is a process flow diagram illustrating an example embodiment of asystem and method for image localization based on semantic segmentation;and

FIG. 5 shows a diagrammatic representation of machine in the exampleform of a computer system within which a set of instructions whenexecuted may cause the machine to perform any one or more of themethodologies discussed herein.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the various embodiments. It will be evident, however,to one of ordinary skill in the art that the various embodiments may bepracticed without these specific details.

As described in various example embodiments, a system and method forimage localization based on semantic segmentation are described herein.An example embodiment disclosed herein can be used in the context of acontrol system 150 in a vehicle ecosystem 101. In one exampleembodiment, a control system 150 with an image processing andlocalization module 200 resident in a vehicle 105 can be configured likethe architecture and ecosystem 101 illustrated in FIG. 1. However, itwill be apparent to those of ordinary skill in the art that the imageprocessing and localization module 200 described and claimed herein canbe implemented, configured, and used in a variety of other applicationsand systems as well.

Referring now to FIG. 1, a block diagram illustrates an exampleecosystem 101 in which a control system 150 and an image processing andlocalization module 200 of an example embodiment can be implemented.These components are described in more detail below. Ecosystem 101includes a variety of systems and components that can generate and/ordeliver one or more sources of information/data and related services tothe control system 150 and the image processing and localization module200, which can be installed in the vehicle 105. For example, a camerainstalled in the vehicle 105, as one of the devices of vehiclesubsystems 140, can generate image and timing data that can be receivedby the control system 150. The control system 150 and the imageprocessing and localization module 200 executing thereon can receivethis image and timing data input. As described in more detail below, theimage processing and localization module 200 can process the input imagedata, generate a semantic label image based on the input image data,remove extraneous dynamic objects from the semantic label image, andcompare the semantic label image to a baseline semantic label image. Theresults of the comparison can be used to accurately determine a preciselocation of the autonomous vehicle. The location information can be usedby an autonomous vehicle control subsystem, as another one of thesubsystems of vehicle subsystems 140. The autonomous vehicle controlsubsystem, for example, can use the vehicle location information tosafely and efficiently navigate the vehicle 105 through a real worlddriving scenario while avoiding obstacles and safely controlling thevehicle.

In an example embodiment as described herein, the control system 150 canbe in data communication with a plurality of vehicle subsystems 140, allof which can be resident in a user's vehicle 105. A vehicle subsysteminterface 141 is provided to facilitate data communication between thecontrol system 150 and the plurality of vehicle subsystems 140. Thecontrol system 150 can be configured to include a data processor 171 toexecute the image processing and localization module 200 for processingimage data received from one or more of the vehicle subsystems 140. Thedata processor 171 can be combined with a data storage device 172 aspart of a computing system 170 in the control system 150. The datastorage device 172 can be used to store data, processing parameters, anddata processing instructions. A processing module interface 165 can beprovided to facilitate data communications between the data processor171 and the image processing and localization module 200. In variousexample embodiments, a plurality of processing modules, configuredsimilarly to image processing and localization module 200, can beprovided for execution by data processor 171. As shown by the dashedlines in FIG. 1, the image processing and localization module 200 can beintegrated into the control system 150 or optionally downloaded to thecontrol system 150.

The control system 150 can be configured to receive or transmit datafrom/to a wide-area network 120 and network resources 122 connectedthereto. A web-enabled device 130 and/or a user mobile device 132 can beused to communicate via network 120. A web-enabled device interface 131can be used by the control system 150 to facilitate data communicationbetween the control system 150 and the network 120 via the web-enableddevice 130. Similarly, a user mobile device interface 133 can be used bythe control system 150 to facilitate data communication between thecontrol system 150 and the network 120 via the user mobile device 132.In this manner, the control system 150 can obtain real-time access tonetwork resources 122 via network 120. The network resources 122 can beused to obtain processing modules for execution by data processor 171,data content to train internal neural networks, system parameters, orother data.

The ecosystem 101 can include a wide area data network 120. The network120 represents one or more conventional wide area data networks, such asthe Internet, a cellular telephone network, satellite network, pagernetwork, a wireless broadcast network, gaming network, WiFi network,peer-to-peer network, Voice over IP (VoIP) network, etc. One or more ofthese networks 120 can be used to connect a user or client system withnetwork resources 122, such as websites, servers, central control sites,or the like. The network resources 122 can generate and/or distributedata, which can be received in vehicle 105 via web-enabled devices 130or user mobile devices 132. The network resources 122 can also hostnetwork cloud services, which can support the functionality used tocompute or assist in processing image input or image input analysis.Antennas can serve to connect the control system 150 and the imageprocessing and localization module 200 with the data network 120 viacellular, satellite, radio, or other conventional signal receptionmechanisms. Such cellular data networks are currently available (e.g.,Verizon™, AT&T™, T-Mobile™, etc.). Such satellite-based data or contentnetworks are also currently available (e.g., SiriusXM™, HughesNet™,etc.). The conventional broadcast networks, such as AM/FM radionetworks, pager networks, UHF networks, gaming networks, WiFi networks,peer-to-peer networks, Voice over IP (VoIP) networks, and the like arealso well-known. Thus, as described in more detail below, the controlsystem 150 and the image processing and localization module 200 canreceive web-based data or content via an web-enabled device interface131, which can be used to connect with the web-enabled device receiver130 and network 120. In this manner, the control system 150 and theimage processing and localization module 200 can support a variety ofnetwork-connectable devices and systems from within a vehicle 105.

As shown in FIG. 1, the control system 150 and the image processing andlocalization module 200 can also receive data, image processing controlparameters, and training content from user mobile devices 132, which canbe located inside or proximately to the vehicle 105. The user mobiledevices 132 can represent standard mobile devices, such as cellularphones, smartphones, personal digital assistants (PDA's), MP3 players,tablet computing devices (e.g., iPad™), laptop computers, CD players,and other mobile devices, which can produce, receive, and/or deliverdata, image processing control parameters, and content for the controlsystem 150 and the image processing and localization module 200. Asshown in FIG. 1, the mobile devices 132 can also be in datacommunication with the network cloud 120. The mobile devices 132 cansource data and content from internal memory components of the mobiledevices 132 themselves or from network resources 122 via network 120.Additionally, mobile devices 132 can themselves include a GPS datareceiver, accelerometers, WiFi triangulation, or other geo-locationsensors or components in the mobile device, which can be used todetermine the real-time geo-location of the user (via the mobile device)at any moment in time. In any case, the control system 150 and the imageprocessing and localization module 200 can receive data from the mobiledevices 132 as shown in FIG. 1.

Referring still to FIG. 1, the example embodiment of ecosystem 101 caninclude vehicle operational subsystems 140. For embodiments that areimplemented in a vehicle 105, many standard vehicles include operationalsubsystems, such as electronic control units (ECUs), supportingmonitoring/control subsystems for the engine, brakes, transmission,electrical system, emissions system, interior environment, and the like.For example, data signals communicated from the vehicle operationalsubsystems 140 (e.g., ECUs of the vehicle 105) to the control system 150via vehicle subsystem interface 141 may include information about thestate of one or more of the components or subsystems of the vehicle 105.In particular, the data signals, which can be communicated from thevehicle operational subsystems 140 to a Controller Area Network (CAN)bus of the vehicle 105, can be received and processed by the controlsystem 150 via vehicle subsystem interface 141. Embodiments of thesystems and methods described herein can be used with substantially anymechanized system that uses a CAN bus or similar data communications busas defined herein, including, but not limited to, industrial equipment,boats, trucks, machinery, or automobiles; thus, the term “vehicle” asused herein can include any such mechanized systems. Embodiments of thesystems and methods described herein can also be used with any systemsemploying some form of network data communications; however, suchnetwork communications are not required.

Referring still to FIG. 1, the example embodiment of ecosystem 101, andthe vehicle operational subsystems 140 therein, can include a variety ofvehicle subsystems in support of the operation of vehicle 105. Ingeneral, the vehicle 105 may take the form of a car, truck, motorcycle,bus, boat, airplane, helicopter, lawn mower, earth mover, snowmobile,aircraft, recreational vehicle, amusement park vehicle, farm equipment,construction equipment, tram, golf cart, train, and trolley, forexample. Other vehicles are possible as well. The vehicle 105 may beconfigured to operate fully or partially in an autonomous mode. Forexample, the vehicle 105 may control itself while in the autonomousmode, and may be operable to determine a current state of the vehicleand its environment, determine a predicted behavior of at least oneother vehicle in the environment, determine a confidence level that maycorrespond to a likelihood of the at least one other vehicle to performthe predicted behavior, and control the vehicle 105 based on thedetermined information. While in autonomous mode, the vehicle 105 may beconfigured to operate without human interaction.

The vehicle 105 may include various vehicle subsystems such as a vehicledrive subsystem 142, vehicle sensor subsystem 144, vehicle controlsubsystem 146, and occupant interface subsystem 148. As described above,the vehicle 105 may also include the control system 150, the computingsystem 170, and the image processing and localization module 200. Thevehicle 105 may include more or fewer subsystems and each subsystemcould include multiple elements. Further, each of the subsystems andelements of vehicle 105 could be interconnected. Thus, one or more ofthe described functions of the vehicle 105 may be divided up intoadditional functional or physical components or combined into fewerfunctional or physical components. In some further examples, additionalfunctional and physical components may be added to the examplesillustrated by FIG. 1.

The vehicle drive subsystem 142 may include components operable toprovide powered motion for the vehicle 105. In an example embodiment,the vehicle drive subsystem 142 may include an engine or motor,wheels/tires, a transmission, an electrical subsystem, and a powersource. The engine or motor may be any combination of an internalcombustion engine, an electric motor, steam engine, fuel cell engine,propane engine, or other types of engines or motors. In some exampleembodiments, the engine may be configured to convert a power source intomechanical energy. In some example embodiments, the vehicle drivesubsystem 142 may include multiple types of engines or motors. Forinstance, a gas-electric hybrid car could include a gasoline engine andan electric motor. Other examples are possible.

The wheels of the vehicle 105 may be standard tires. The wheels of thevehicle 105 may be configured in various formats, including a unicycle,bicycle, tricycle, or a four-wheel format, such as on a car or a truck,for example. Other wheel geometries are possible, such as thoseincluding six or more wheels. Any combination of the wheels of vehicle105 may be operable to rotate differentially with respect to otherwheels. The wheels may represent at least one wheel that is fixedlyattached to the transmission and at least one tire coupled to a rim ofthe wheel that could make contact with the driving surface. The wheelsmay include a combination of metal and rubber, or another combination ofmaterials. The transmission may include elements that are operable totransmit mechanical power from the engine to the wheels. For thispurpose, the transmission could include a gearbox, a clutch, adifferential, and drive shafts. The transmission may include otherelements as well. The drive shafts may include one or more axles thatcould be coupled to one or more wheels. The electrical system mayinclude elements that are operable to transfer and control electricalsignals in the vehicle 105. These electrical signals can be used toactivate lights, servos, electrical motors, and other electricallydriven or controlled devices of the vehicle 105. The power source mayrepresent a source of energy that may, in full or in part, power theengine or motor. That is, the engine or motor could be configured toconvert the power source into mechanical energy. Examples of powersources include gasoline, diesel, other petroleum-based fuels, propane,other compressed gas-based fuels, ethanol, fuel cell, solar panels,batteries, and other sources of electrical power. The power source couldadditionally or alternatively include any combination of fuel tanks,batteries, capacitors, or flywheels. The power source may also provideenergy for other subsystems of the vehicle 105.

The vehicle sensor subsystem 144 may include a number of sensorsconfigured to sense information about an environment or condition of thevehicle 105. For example, the vehicle sensor subsystem 144 may includean inertial measurement unit (IMU), a Global Positioning System (GPS)transceiver, a RADAR unit, a laser range finder/LIDAR unit (or otherdistance measuring device), and one or more cameras or image capturingdevices. The vehicle sensor subsystem 144 may also include sensorsconfigured to monitor internal systems of the vehicle 105 (e.g., an O2monitor, a fuel gauge, an engine oil temperature). Other sensors arepossible as well. One or more of the sensors included in the vehiclesensor subsystem 144 may be configured to be actuated separately orcollectively in order to modify a position, an orientation, or both, ofthe one or more sensors.

The IMU may include any combination of sensors (e.g., accelerometers andgyroscopes) configured to sense position and orientation changes of thevehicle 105 based on inertial acceleration. The GPS transceiver may beany sensor configured to estimate a geographic location of the vehicle105. For this purpose, the GPS transceiver may include areceiver/transmitter operable to provide information regarding theposition of the vehicle 105 with respect to the Earth. The RADAR unitmay represent a system that utilizes radio signals to sense objectswithin the local environment of the vehicle 105. In some embodiments, inaddition to sensing the objects, the RADAR unit may additionally beconfigured to sense the speed and the heading of the objects proximateto the vehicle 105. The laser range finder or LIDAR unit (or otherdistance measuring device) may be any sensor configured to sense objectsin the environment in which the vehicle 105 is located using lasers. Inan example embodiment, the laser range finder/LIDAR unit may include oneor more laser sources, a laser scanner, and one or more detectors, amongother system components. The laser range finder/LIDAR unit could beconfigured to operate in a coherent (e.g., using heterodyne detection)or an incoherent detection mode. The cameras may include one or moredevices configured to capture a plurality of images of the environmentof the vehicle 105. The cameras may be still image cameras or motionvideo cameras.

The vehicle control system 146 may be configured to control operation ofthe vehicle 105 and its components. Accordingly, the vehicle controlsystem 146 may include various elements such as a steering unit, athrottle, a brake unit, a navigation unit, and an autonomous controlunit.

The steering unit may represent any combination of mechanisms that maybe operable to adjust the heading of vehicle 105. The throttle may beconfigured to control, for instance, the operating speed of the engineand, in turn, control the speed of the vehicle 105. The brake unit caninclude any combination of mechanisms configured to decelerate thevehicle 105. The brake unit can use friction to slow the wheels in astandard manner. In other embodiments, the brake unit may convert thekinetic energy of the wheels to electric current. The brake unit maytake other forms as well. The navigation unit may be any systemconfigured to determine a driving path or route for the vehicle 105. Thenavigation unit may additionally be configured to update the drivingpath dynamically while the vehicle 105 is in operation. In someembodiments, the navigation unit may be configured to incorporate datafrom the image processing and localization module 200, the GPStransceiver, and one or more predetermined maps so as to determine thedriving path for the vehicle 105. The autonomous control unit mayrepresent a control system configured to identify, evaluate, and avoidor otherwise negotiate potential obstacles in the environment of thevehicle 105. In general, the autonomous control unit may be configuredto control the vehicle 105 for operation without a driver or to providedriver assistance in controlling the vehicle 105. In some embodiments,the autonomous control unit may be configured to incorporate data fromthe image processing and localization module 200, the GPS transceiver,the RADAR, the LIDAR, the cameras, and other vehicle subsystems todetermine the driving path or trajectory for the vehicle 105. Thevehicle control system 146 may additionally or alternatively includecomponents other than those shown and described.

Occupant interface subsystems 148 may be configured to allow interactionbetween the vehicle 105 and external sensors, other vehicles, othercomputer systems, and/or an occupant or user of vehicle 105. Forexample, the occupant interface subsystems 148 may include standardvisual display devices (e.g., plasma displays, liquid crystal displays(LCDs), touchscreen displays, heads-up displays, or the like), speakersor other audio output devices, microphones or other audio input devices,navigation interfaces, and interfaces for controlling the internalenvironment (e.g., temperature, fan, etc.) of the vehicle 105.

In an example embodiment, the occupant interface subsystems 148 mayprovide, for instance, means for a user/occupant of the vehicle 105 tointeract with the other vehicle subsystems. The visual display devicesmay provide information to a user of the vehicle 105. The user interfacedevices can also be operable to accept input from the user via atouchscreen. The touchscreen may be configured to sense at least one ofa position and a movement of a user's finger via capacitive sensing,resistance sensing, or a surface acoustic wave process, among otherpossibilities. The touchscreen may be capable of sensing finger movementin a direction parallel or planar to the touchscreen surface, in adirection normal to the touchscreen surface, or both, and may also becapable of sensing a level of pressure applied to the touchscreensurface. The touchscreen may be formed of one or more translucent ortransparent insulating layers and one or more translucent or transparentconducting layers. The touchscreen may take other forms as well.

In other instances, the occupant interface subsystems 148 may providemeans for the vehicle 105 to communicate with devices within itsenvironment. The microphone may be configured to receive audio (e.g., avoice command or other audio input) from a user of the vehicle 105.Similarly, the speakers may be configured to output audio to a user ofthe vehicle 105. In one example embodiment, the occupant interfacesubsystems 148 may be configured to wirelessly communicate with one ormore devices directly or via a communication network. For example, awireless communication system could use 3G cellular communication, suchas CDMA, EVDO, GSM/GPRS, or 4G cellular communication, such as WiMAX orLTE. Alternatively, the wireless communication system may communicatewith a wireless local area network (WLAN), for example, using WIFI®. Insome embodiments, the wireless communication system 146 may communicatedirectly with a device, for example, using an infrared link, BLUETOOTH®,or ZIGBEE®. Other wireless protocols, such as various vehicularcommunication systems, are possible within the context of thedisclosure. For example, the wireless communication system may includeone or more dedicated short range communications (DSRC) devices that mayinclude public or private data communications between vehicles and/orroadside stations.

Many or all of the functions of the vehicle 105 can be controlled by thecomputing system 170. The computing system 170 may include at least onedata processor 171 (which can include at least one microprocessor) thatexecutes processing instructions stored in a non-transitory computerreadable medium, such as the data storage device 172. The computingsystem 170 may also represent a plurality of computing devices that mayserve to control individual components or subsystems of the vehicle 105in a distributed fashion. In some embodiments, the data storage device172 may contain processing instructions (e.g., program logic) executableby the data processor 171 to perform various functions of the vehicle105, including those described herein in connection with the drawings.The data storage device 172 may contain additional instructions as well,including instructions to transmit data to, receive data from, interactwith, or control one or more of the vehicle drive subsystem 140, thevehicle sensor subsystem 144, the vehicle control subsystem 146, and theoccupant interface subsystems 148.

In addition to the processing instructions, the data storage device 172may store data such as image processing parameters, training data,roadway maps, and path information, among other information. Suchinformation may be used by the vehicle 105 and the computing system 170during the operation of the vehicle 105 in the autonomous,semi-autonomous, and/or manual modes.

The vehicle 105 may include a user interface for providing informationto or receiving input from a user or occupant of the vehicle 105. Theuser interface may control or enable control of the content and thelayout of interactive images that may be displayed on a display device.Further, the user interface may include one or more input/output deviceswithin the set of occupant interface subsystems 148, such as the displaydevice, the speakers, the microphones, or a wireless communicationsystem.

The computing system 170 may control the function of the vehicle 105based on inputs received from various vehicle subsystems (e.g., thevehicle drive subsystem 140, the vehicle sensor subsystem 144, and thevehicle control subsystem 146), as well as from the occupant interfacesubsystem 148. For example, the computing system 170 may use input fromthe vehicle control system 146 in order to control the steering unit toavoid an obstacle detected by the vehicle sensor subsystem 144 and theimage processing and localization module 200. In an example embodiment,the computing system 170 can be operable to provide control over manyaspects of the vehicle 105 and its subsystems.

Although FIG. 1 shows various components of vehicle 105, e.g., vehiclesubsystems 140, computing system 170, data storage device 172, controlsystem 150, and image processing and localization module 200, as beingintegrated into the vehicle 105, one or more of these components couldbe mounted or associated separately from the vehicle 105. For example,data storage device 172 could, in part or in full, exist separate fromthe vehicle 105. Thus, the vehicle 105 could be provided in the form ofdevice elements that may be located separately or together. The deviceelements that make up vehicle 105 could be communicatively coupledtogether in a wired or wireless fashion. In various example embodiments,the control system 150 and the image processing and localization module200 in data communication therewith can be implemented as integratedcomponents or as separate components. In an example embodiment, thesoftware components of the control system 150 and/or the imageprocessing and localization module 200 can be dynamically upgraded,modified, and/or augmented by use of the data connection with the mobiledevices 132 and/or the network resources 122 via network 120. Thecontrol system 150 can periodically query a mobile device 132 or anetwork resource 122 for updates or updates can be pushed to the controlsystem 150.

In the example embodiment, the image processing and localization module200 can be configured to include an interface with the control system150, as shown in FIG. 1, through which the image processing andlocalization module 200 can send and receive data as described herein.Additionally, the image processing and localization module 200 can beconfigured to include an interface with the control system 150 and/orother ecosystem 101 subsystems through which the image processing andlocalization module 200 can receive ancillary data from the various datasources described above. The ancillary data can be used to augment,modify, or train the operation of the image processing and localizationmodule 200 based on a variety of factors including, the context in whichthe user is operating the vehicle (e.g., the location of the vehicle,the specified destination, direction of travel, speed, the time of day,the status of the vehicle, etc.), and a variety of other data obtainablefrom the variety of sources, local and remote, as described herein. Asdescribed above, the image processing and localization module 200 canalso be implemented in systems and platforms that are not deployed in avehicle and not necessarily used in or with a vehicle.

System and Method for Image Localization Based on Semantic Segmentation

Various example embodiments disclosed herein describe a system andmethod for highly automated localization and navigation for autonomousvehicles using semantic parsing. The system and method of an exampleembodiment comprise two main components or phases: 1) a mappingcomponent/phase, and 2) a localization component/phase. In the mappingphase, image data from one or more cameras (or other image generatingdevices) is sent to a computing device within the system while a testvehicle is operating. The image data can correspond to at least oneframe from a video stream generated by one or more cameras. Thecomputing device processes the image data to produce a highly accuratebaseline semantic label image and remove extraneous dynamic objects fromthe baseline semantic label image, which is recorded as a baselinesemantic label map or a route map representation in a data storagedevice. In the localization phase, a second computing device in anautonomous vehicle computes the same semantic label image based on liveimage data and removes extraneous dynamic objects from the semanticlabel image. The second computing device then localizes the vehicle'sposition by comparing the similarity of the semantic label image to thebaseline semantic label map. The method disclosed herein includes: 1)removing extraneous dynamic objects from a semantic label image; and 2)localizing a vehicle's position by comparing the similarity of asemantic label image to a baseline semantic label map.

Referring now to FIGS. 2 and 3, diagrams illustrate the system forlocalization based on semantic segmentation 201/301 in an exampleembodiment. The system for localization based on semantic segmentation201 as shown in FIG. 2 is a configuration used by a test vehicle togenerate baseline semantic label image data. The system for localizationbased on semantic segmentation 301 as shown in FIG. 3 is a configurationused by an autonomous vehicle in a real world scenario to generatesemantic label image data from live vehicle image data, compare thegenerated semantic label image data with the baseline semantic labelimage map, and determine a precise vehicle location from the comparison.These systems of an example embodiment are described in more detailbelow.

In an example embodiment as shown in FIG. 2, the system for localizationbased on semantic segmentation 201, and the baseline image processingand localization module 202 included therein, is a configuration used bya test vehicle to generate baseline semantic label image data. In theexample embodiment, the baseline image processing and localizationmodule 202 can be configured to include the image semantic segmentationmodule 273 and the semantic label image processing module 275, as wellas other processing modules not shown for clarity. Each of these modulescan be implemented as software, firmware, or other logic componentsexecuting or activated within an executable environment of the baselineimage processing and localization module 202 operating within or in datacommunication with the control system 150. Each of these modules of anexample embodiment is described in more detail below in connection withthe figures provided herein.

Referring still to FIG. 2, the image semantic segmentation module 273,operating in a baseline generation configuration, can receive image data210 from a test vehicle equipped with one or more cameras or other imagecapturing devices. The test vehicle can also be equipped with a highprecision global positioning system (GPS), high quality LIDAR and Radarsystems, and other devices configured to accurately generate images of aparticular location and correlate the images with highly precisedistance and location measurements. As a result, the test vehicle cantransit on a defined path in an environment of interest and collectimages of the environment along with precise distance and locationmeasurements of objects in the environment. The image semanticsegmentation module 273 can collect this image data and thecorresponding distance and location measurement data. The image data canbe correlated with the corresponding distance and location measurementdata to produce a highly accurate three-dimensional (3D) model or map ofthe environment. Using the image data 210, the image semanticsegmentation module 273 can perform semantic segmentation or otherobject detection techniques on the collected images 210 to identify andlabel objects in the image data. Using the correlated distance andlocation measurement data, the 3D locations of the identified objectscan also be defined with a high degree of accuracy. As a plurality ofimages are processed in this way, the locations of identified objectscan be tracked over a pre-defined time interval. The differences inposition for each of the identified objects can be used to derive avelocity and velocity vector for each of the moving or dynamic objectsin the images. In this manner, the image semantic segmentation module273 can generate semantic label image data including the object labels,the accurate locations of the objects identified in the images collectedby the test vehicle, and the velocities of the moving or dynamicobjects. This information can be used by the semantic label imageprocessing module 275.

Referring still to FIG. 2, the semantic label image processing module275, operating in a baseline generation configuration, can receive thesemantic label image data and the detected object positions andvelocities from the image semantic segmentation module 273. The semanticlabel image processing module 275 processes the semantic label imagedata to identify the dynamic (e.g., moving), fleeting, transitory, orother extraneous objects identified in the image data. For example, thedynamic, fleeting, transitory, or other extraneous objects in the imagecan be objects that do not contribute to the deterministic and accuratelocalization of the image and other image objects. The semantic labelimage processing module 275 can identify these dynamic, fleeting,transitory, or other extraneous objects in the semantic label image dataand remove them from the semantic label image data. The resultingsemantic label image data provides a highly accurate semantic label mapthat includes identified and labeled objects, which are deterministicand provide accurate localization. This highly accurate semantic labelmap can serve as a baseline semantic label map 220, from which accuratevehicle localization can be determined. The baseline semantic label map220 can be stored in memory 272 as route map representations 274, whichcan be used by autonomous vehicle control systems in real world drivingscenarios to assist the vehicle control systems to determine accuratevehicle locations.

Referring now to FIG. 3, the system for localization based on semanticsegmentation 301, and the image processing and localization module 200included therein, provides a configuration used by an autonomous vehiclein live vehicle operation in a real world scenario to generate semanticlabel image data from live vehicle image data 310, compare the generatedsemantic label image data with the baseline semantic label map 220, anddetermine a precise vehicle location 320 from the comparison. In theexample embodiment, the image processing and localization module 200 canbe configured to include the image semantic segmentation module 273, thesemantic label image processing module 275, a localization processingmodule 377, as well as other processing modules not shown for clarity.Each of these modules can be implemented as software, firmware, or otherlogic components executing or activated within an executable environmentof the image processing and localization module 200 operating within orin data communication with the control system 150. Each of these modulesof example embodiments is described in detail below in connection withthe figures provided herein.

Referring still to FIG. 3, the image semantic segmentation module 273,operating in a live vehicle operation configuration, can receive imagedata 310 from an autonomous vehicle operating in a real world scenario.The autonomous vehicle can be equipped with the standard cameras orimaging devices, GPS, LIDAR, Radar, and other vehicle sensor devices andsubsystems as described above. The autonomous vehicle can use the sensorsubsystems to collect images of the environment along with distancemeasurements of objects in the environment from the cameras and LIDAR orRadar devices. Because the sensor subsystems of the autonomous vehiclemay not be highly precise and the real world environment may not be ascontrolled as the test environment, the images and distance datacollected by the autonomous vehicle may not be accurate or complete. Asa result, the location of objects detected in the environment and thus,localization in the environment may not be highly accurate.Nevertheless, the image semantic segmentation module 273 can collectthis image data 310 and the corresponding distance data from theautonomous vehicle sensor subsystems. Using the image data 310, theimage semantic segmentation module 273 can perform semantic segmentationor other object detection techniques on the collected images 310 toidentify and label objects in the image data. Using the distance data,the 3D locations of the identified objects can also be estimated. As aplurality of images are processed in this way, the locations ofidentified objects can be tracked over a pre-defined time interval. Thedifferences in position for each of the identified objects can be usedto derive an estimated velocity and velocity vector for each of themoving objects in the images. In this manner, the image semanticsegmentation module 273 can generate semantic label image data includingthe object labels, the estimated locations of the objects identified inthe images collected by the autonomous vehicle, and the estimatedvelocities of the moving objects. This information can be used by thesemantic label image processing module 275.

Referring still to FIG. 3, the semantic label image processing module275, operating in a live vehicle operation configuration, can receivethe semantic label image data and the detected object positions andvelocities from the image semantic segmentation module 273. The semanticlabel image processing module 275 processes the semantic label imagedata to identify the dynamic (e.g., moving) dynamic, fleeting,transitory, or other extraneous objects identified in the image data.For example, the dynamic, fleeting, transitory, or other extraneousobjects in the image can be objects that do not contribute to thedeterministic and accurate localization of the image and other imageobjects. The semantic label image processing module 275 can identifythese dynamic, fleeting, transitory, or other extraneous objects in thesemantic label image data and remove them from the semantic label imagedata. The resulting semantic label image data provides a semantic labelmap that includes identified and labeled objects, which aredeterministic and useful for vehicle localization. However, as describedabove, the identified objects and locations generated during livevehicle operation may not be highly accurate. As a result, it may bedifficult to determine a precise vehicle location from this semanticlabel map data. Thus, an example embodiment provides a localizationprocessing module 377 to further process the semantic label image dataand extract a precise vehicle location therefrom.

As described above, a highly accurate semantic label map, producedduring a baseline generation phase, can serve as a baseline semanticlabel map 220, from which accurate vehicle localization can bedetermined. The baseline semantic label map 220 can be retrieved from amemory 272 and stored in a local memory 372 as route map representations374, which can be used by the localization processing module 377 togenerate an accurate vehicle location. In particular, the localizationprocessing module 377 can use the semantic label image data generatedfrom the live image data 310 and compare the generated semantic labelimage data with the route map representations 374 corresponding to thebaseline semantic label maps 220. Because the dynamic, fleeting,transitory, or other extraneous objects in each of the generatedsemantic label image data and the baseline semantic label maps 220 havebeen removed, it is faster and more efficient to compare the generatedsemantic label image data with the baseline semantic label maps 220 andto find a matching baseline semantic label map 220. As described above,the baseline semantic label maps 220 are generated with highly precise3D position data. Thus, when the localization processing module 377performs the comparison and finds a matching baseline semantic label map220, the precise 3D position data can be extracted from the matchingbaseline semantic label map 220 and used to generate a highly accurate3D position 320 of the autonomous vehicle. As this accurate 3D positionis tracked over a plurality of cycles, an accurate velocity and velocityvector for the autonomous vehicle can also be generated. Additionally,an accurate vehicle track or position history can also be generated toaccurately plot the path of the vehicle through the environment. Thevehicle location 320, generated by the localization processing module377, can be provided as an output from the image processing andlocalization module 200. The vehicle location 320 can be used byautonomous vehicle control systems in real world driving scenarios toassist the vehicle control systems to safely and efficiently operate theautonomous vehicle in a variety of different environments.

Referring now to FIG. 4, a flow diagram illustrates an exampleembodiment of a system and method 1000 for image localization based onsemantic segmentation. The example embodiment can be configured for:receiving image data from an image generating device mounted on anautonomous vehicle (processing block 1010); performing semanticsegmentation or other object detection on the received image data toidentify and label objects in the image data and produce semantic labelimage data (processing block 1020); identifying extraneous objects inthe semantic label image data (processing block 1030); removing theextraneous objects from the semantic label image data (processing block1040); comparing the semantic label image data to a baseline semanticlabel map (processing block 1050); and determining a vehicle location ofthe autonomous vehicle based on information in a matching baselinesemantic label map (processing block 1060).

As used herein and unless specified otherwise, the term “mobile device”includes any computing or communications device that can communicatewith the control system 150 and/or the image processing and localizationmodule 200 described herein to obtain read or write access to datasignals, messages, or content communicated via any mode of datacommunications. In many cases, the mobile device 130 is a handheld,portable device, such as a smart phone, mobile phone, cellulartelephone, tablet computer, laptop computer, display pager, radiofrequency (RF) device, infrared (IR) device, global positioning device(GPS), Personal Digital Assistants (PDA), handheld computers, wearablecomputer, portable game console, other mobile communication and/orcomputing device, or an integrated device combining one or more of thepreceding devices, and the like. Additionally, the mobile device 130 canbe a computing device, personal computer (PC), multiprocessor system,microprocessor-based or programmable consumer electronic device, networkPC, diagnostics equipment, a system operated by a vehicle 119manufacturer or service technician, and the like, and is not limited toportable devices. The mobile device 130 can receive and process data inany of a variety of data formats. The data format may include or beconfigured to operate with any programming format, protocol, or languageincluding, but not limited to, JavaScript, C++, iOS, Android, etc.

As used herein and unless specified otherwise, the term “networkresource” includes any device, system, or service that can communicatewith the control system 150 and/or the image processing and localizationmodule 200 described herein to obtain read or write access to datasignals, messages, or content communicated via any mode of inter-processor networked data communications. In many cases, the network resource122 is a data network accessible computing platform, including client orserver computers, websites, mobile devices, peer-to-peer (P2P) networknodes, and the like. Additionally, the network resource 122 can be a webappliance, a network router, switch, bridge, gateway, diagnosticsequipment, a system operated by a vehicle 119 manufacturer or servicetechnician, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” can also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein. Thenetwork resources 122 may include any of a variety of providers orprocessors of network transportable digital content. Typically, the fileformat that is employed is Extensible Markup Language (XML), however,the various embodiments are not so limited, and other file formats maybe used. For example, data formats other than Hypertext Markup Language(HTML)/XML or formats other than open/standard data formats can besupported by various embodiments. Any electronic file format, such asPortable Document Format (PDF), audio (e.g., Motion Picture ExpertsGroup Audio Layer 3—MP3, and the like), video (e.g., MP4, and the like),and any proprietary interchange format defined by specific content sitescan be supported by the various embodiments described herein.

The wide area data network 120 (also denoted the network cloud) usedwith the network resources 122 can be configured to couple one computingor communication device with another computing or communication device.The network may be enabled to employ any form of computer readable dataor media for communicating information from one electronic device toanother. The network 120 can include the Internet in addition to otherwide area networks (WANs), cellular telephone networks, metro-areanetworks, local area networks (LANs), other packet-switched networks,circuit-switched networks, direct data connections, such as through auniversal serial bus (USB) or Ethernet port, other forms ofcomputer-readable media, or any combination thereof. The network 120 caninclude the Internet in addition to other wide area networks (WANs),cellular telephone networks, satellite networks, over-the-air broadcastnetworks, AM/FM radio networks, pager networks, UHF networks, otherbroadcast networks, gaming networks, WiFi networks, peer-to-peernetworks, Voice Over IP (VoIP) networks, metro-area networks, local areanetworks (LANs), other packet-switched networks, circuit-switchednetworks, direct data connections, such as through a universal serialbus (USB) or Ethernet port, other forms of computer-readable media, orany combination thereof. On an interconnected set of networks, includingthose based on differing architectures and protocols, a router orgateway can act as a link between networks, enabling messages to be sentbetween computing devices on different networks. Also, communicationlinks within networks can typically include twisted wire pair cabling,USB, Firewire, Ethernet, or coaxial cable, while communication linksbetween networks may utilize analog or digital telephone lines, full orfractional dedicated digital lines including T1, T2, T3, and T4,Integrated Services Digital Networks (ISDNs), Digital User Lines (DSLs),wireless links including satellite links, cellular telephone links, orother communication links known to those of ordinary skill in the art.Furthermore, remote computers and other related electronic devices canbe remotely connected to the network via a modem and temporary telephonelink.

The network 120 may further include any of a variety of wirelesssub-networks that may further overlay stand-alone ad-hoc networks, andthe like, to provide an infrastructure-oriented connection. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. The network may also include anautonomous system of terminals, gateways, routers, and the likeconnected by wireless radio links or wireless transceivers. Theseconnectors may be configured to move freely and randomly and organizethemselves arbitrarily, such that the topology of the network may changerapidly. The network 120 may further employ one or more of a pluralityof standard wireless and/or cellular protocols or access technologiesincluding those set forth herein in connection with network interface712 and network 714 described in the figures herewith.

In a particular embodiment, a mobile device 132 and/or a networkresource 122 may act as a client device enabling a user to access anduse the control system 150 and/or the image processing and localizationmodule 200 to interact with one or more components of a vehiclesubsystem. These client devices 132 or 122 may include virtually anycomputing device that is configured to send and receive information overa network, such as network 120 as described herein. Such client devicesmay include mobile devices, such as cellular telephones, smart phones,tablet computers, display pagers, radio frequency (RF) devices, infrared(IR) devices, global positioning devices (GPS), Personal DigitalAssistants (PDAs), handheld computers, wearable computers, gameconsoles, integrated devices combining one or more of the precedingdevices, and the like. The client devices may also include othercomputing devices, such as personal computers (PCs), multiprocessorsystems, microprocessor-based or programmable consumer electronics,network PC's, and the like. As such, client devices may range widely interms of capabilities and features. For example, a client deviceconfigured as a cell phone may have a numeric keypad and a few lines ofmonochrome LCD display on which only text may be displayed. In anotherexample, a web-enabled client device may have a touch sensitive screen,a stylus, and a color LCD display screen in which both text and graphicsmay be displayed. Moreover, the web-enabled client device may include abrowser application enabled to receive and to send wireless applicationprotocol messages (WAP), and/or wired application messages, and thelike. In one embodiment, the browser application is enabled to employHyperText Markup Language (HTML), Dynamic HTML, Handheld Device MarkupLanguage (HDML), Wireless Markup Language (WML), WMLScript, JavaScript™,EXtensible HTML (xHTML), Compact HTML (CHTML), and the like, to displayand send a message with relevant information.

The client devices may also include at least one client application thatis configured to receive content or messages from another computingdevice via a network transmission. The client application may include acapability to provide and receive textual content, graphical content,video content, audio content, alerts, messages, notifications, and thelike. Moreover, the client devices may be further configured tocommunicate and/or receive a message, such as through a Short MessageService (SMS), direct messaging (e.g., Twitter), email, MultimediaMessage Service (MMS), instant messaging (IM), internet relay chat(IRC), mIRC, Jabber, Enhanced Messaging Service (EMS), text messaging,Smart Messaging, Over the Air (OTA) messaging, or the like, betweenanother computing device, and the like. The client devices may alsoinclude a wireless application device on which a client application isconfigured to enable a user of the device to send and receiveinformation to/from network resources wirelessly via the network.

The control system 150 and/or the image processing and localizationmodule 200 can be implemented using systems that enhance the security ofthe execution environment, thereby improving security and reducing thepossibility that the control system 150 and/or the image processing andlocalization module 200 and the related services could be compromised byviruses or malware. For example, the control system 150 and/or the imageprocessing and localization module 200 can be implemented using aTrusted Execution Environment, which can ensure that sensitive data isstored, processed, and communicated in a secure way.

FIG. 5 shows a diagrammatic representation of a machine in the exampleform of a computing system 700 within which a set of instructions whenexecuted and/or processing logic when activated may cause the machine toperform any one or more of the methodologies described and/or claimedherein. In alternative embodiments, the machine operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine may operate in the capacity of aserver or a client machine in server-client network environment, or as apeer machine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a laptop computer, a tabletcomputing system, a Personal Digital Assistant (PDA), a cellulartelephone, a smartphone, a web appliance, a set-top box (STB), a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) or activating processing logicthat specify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” can also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions or processing logic to performany one or more of the methodologies described and/or claimed herein.

The example computing system 700 can include a data processor 702 (e.g.,a System-on-a-Chip (SoC), general processing core, graphics core, andoptionally other processing logic) and a memory 704, which cancommunicate with each other via a bus or other data transfer system 706.The mobile computing and/or communication system 700 may further includevarious input/output (I/O) devices and/or interfaces 710, such as atouchscreen display, an audio jack, a voice interface, and optionally anetwork interface 712. In an example embodiment, the network interface712 can include one or more radio transceivers configured forcompatibility with any one or more standard wireless and/or cellularprotocols or access technologies (e.g., 2nd (2G), 2.5, 3rd (3G), 4th(4G) generation, and future generation radio access for cellularsystems, Global System for Mobile communication (GSM), General PacketRadio Services (GPRS), Enhanced Data GSM Environment (EDGE), WidebandCode Division Multiple Access (WCDMA), LTE, CDMA2000, WLAN, WirelessRouter (WR) mesh, and the like). Network interface 712 may also beconfigured for use with various other wired and/or wirelesscommunication protocols, including TCP/IP, UDP, SIP, SMS, RTP, WAP,CDMA, TDMA, UMTS, UWB, WiFi, WiMax, Bluetooth©, IEEE 802.11x, and thelike. In essence, network interface 712 may include or support virtuallyany wired and/or wireless communication and data processing mechanismsby which information/data may travel between a computing system 700 andanother computing or communication system via network 714.

The memory 704 can represent a machine-readable medium on which isstored one or more sets of instructions, software, firmware, or otherprocessing logic (e.g., logic 708) embodying any one or more of themethodologies or functions described and/or claimed herein. The logic708, or a portion thereof, may also reside, completely or at leastpartially within the processor 702 during execution thereof by themobile computing and/or communication system 700. As such, the memory704 and the processor 702 may also constitute machine-readable media.The logic 708, or a portion thereof, may also be configured asprocessing logic or logic, at least a portion of which is partiallyimplemented in hardware. The logic 708, or a portion thereof, mayfurther be transmitted or received over a network 714 via the networkinterface 712. While the machine-readable medium of an exampleembodiment can be a single medium, the term “machine-readable medium”should be taken to include a single non-transitory medium or multiplenon-transitory media (e.g., a centralized or distributed database,and/or associated caches and computing systems) that store the one ormore sets of instructions. The term “machine-readable medium” can alsobe taken to include any non-transitory medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform any one or more of themethodologies of the various embodiments, or that is capable of storing,encoding or carrying data structures utilized by or associated with sucha set of instructions. The term “machine-readable medium” canaccordingly be taken to include, but not be limited to, solid-statememories, optical media, and magnetic media.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment.

What is claimed is:
 1. A system comprising: a data processor; and animage processing and localization module, executable by the dataprocessor, the image processing and localization module being configuredto perform an image processing and localization operation configured to:receive image data from an image generating device mounted on anautonomous vehicle; perform semantic segmentation on the received imagedata to identify and label objects in the image data and producesemantic label image data, wherein the semantic segmentation assigns anobject label to each pixel in the image data; identify extraneousobjects in the semantic label image data using the object labelsincluded therein, the extraneous objects being dynamic or transitoryobjects in the image data; remove the extraneous objects from thesemantic label image data; compare the semantic label image data to abaseline semantic label map created from semantic segmentation, whereinthe semantic segmentation assigns an object label to each pixel inbaseline image data obtained from an image generating device and theobject labels are included in the baseline semantic label map; anddetermine a vehicle location of the autonomous vehicle based oninformation in a matching baseline semantic label map.
 2. The system ofclaim 1 wherein the operation is further configured to receive distancedata from a distance measuring device mounted on the autonomous vehicle.3. The system of claim 1 wherein the operation is further configured todetermine a path of the autonomous vehicle through an environment basedon information in the matching baseline semantic label map.
 4. Thesystem of claim 1 wherein the image processing and localizationoperation is further configured to determine a vehicle velocity of theautonomous vehicle.
 5. The system of claim 1 wherein the vehicleposition is output to a vehicle control subsystem of the autonomousvehicle, the vehicle control subsystem being used to control a steeringunit of the autonomous vehicle to avoid a detected object.
 6. The systemof claim 1 wherein the operation is further configured to use a testvehicle with highly accurate image generating devices and distancemeasuring devices mounted thereon to collect image data and generate thebaseline semantic label map therefrom.
 7. The system of claim 1 whereinthe operation is further configured to remove extraneous objects fromthe baseline semantic label map.
 8. A method comprising: receiving imagedata from an image generating device mounted on an autonomous vehicle;performing semantic segmentation on the received image data to identifyand label objects in the image data and produce semantic label imagedata, wherein the semantic segmentation assigns an object label to eachpixel in the image data; identifying extraneous objects in the semanticlabel image data using the object labels included therein, theextraneous objects being dynamic or transitory objects in the imagedata; removing the extraneous objects from the semantic label imagedata; comparing the semantic label image data to a baseline semanticlabel map created from semantic segmentation, wherein the semanticsegmentation assigns an object label to each pixel in baseline imagedata obtained from an image generating device and the object labels areincluded in the baseline semantic label map; and determining a vehiclelocation of the autonomous vehicle based on information in a matchingbaseline semantic label map.
 9. The method of claim 8 includingreceiving distance data from a distance measuring device mounted on theautonomous vehicle, wherein the distance measuring device is a LIDAR.10. The method of claim 8 including determining a path of the autonomousvehicle through an environment based on information in the matchingbaseline semantic label map, and including collecting images of theenvironment along the path.
 11. The method of claim 8 includingdetermining a vehicle velocity of the autonomous vehicle, the velocitybeing derived from differences in the vehicle location over time. 12.The method of claim 8 wherein the vehicle position is output to avehicle control subsystem of the autonomous vehicle, the vehicle controlsubsystem being in data communication with a control system integratedinto the autonomous vehicle.
 13. The method of claim 8 includingcollecting image data and generating the baseline semantic label map inan offline process.
 14. A non-transitory machine-useable storage mediumembodying instructions which, when executed by a machine, cause themachine to: receive image data from an image generating device mountedon an autonomous vehicle; perform semantic segmentation on the receivedimage data to identify and label objects in the image data and producesemantic label image data, wherein the semantic segmentation assigns anobject label to each pixel in the image data; identify extraneousobjects in the semantic label image data using the object labelsincluded therein, the extraneous objects being dynamic or transitoryobjects in the image data; remove the extraneous objects from thesemantic label image data; compare the semantic label image data to abaseline semantic label map created from semantic segmentation, whereinthe semantic segmentation assigns an object label to each pixel inbaseline image data obtained from an image generating device and theobject labels are included in the baseline semantic label map; anddetermine a vehicle location of the autonomous vehicle based oninformation in a matching baseline semantic label map.
 15. Thenon-transitory machine-useable storage medium of claim 14 being furtherconfigured to receive distance data from a distance measuring devicemounted on the autonomous vehicle, the distance data being used toproduce a three-dimensional (3D) model or map of an environment in whichthe autonomous vehicle is located.
 16. The non-transitorymachine-useable storage medium of claim 14 being further configured todetermine a path of the autonomous vehicle through an environment basedon information in the matching baseline semantic label map, and to tracka position history of an environment in which the autonomous vehicle islocated.
 17. The non-transitory machine-useable storage medium of claim14 wherein the vehicle position is output to a vehicle control subsystemof the autonomous vehicle via a Controller Area Network (CAN) bus of theautonomous vehicle.
 18. The non-transitory machine-useable storagemedium of claim 14 wherein the instructions are further configured touse a test vehicle with highly accurate image generating devices anddistance measuring devices mounted thereon to collect image data andgenerate the baseline semantic label map therefrom, the test vehicleincluding one or more cameras.
 19. The non-transitory machine-useablestorage medium of claim 18 wherein the test vehicle further includes atleast one of global positioning system (GPS) equipment, LIDAR system,and RADAR system.
 20. The non-transitory machine-useable storage mediumof claim 14 wherein the instructions are further configured to use atest vehicle with highly accurate image generating devices and distancemeasuring devices mounted thereon to collect image data and generate thebaseline semantic label map therefrom, the baseline semantic label mapbeing generated as a route map representation.